👉 Accuracy math is a fundamental concept in machine learning and data science that quantifies how well a model's predictions match the actual outcomes. It measures the proportion of correct predictions out of total predictions made by the model. Typically, accuracy is calculated as the number of correct predictions divided by the total number of predictions (true positives + true negatives), resulting in a fraction. This fraction is then converted into a percentage to express the model's accuracy. For binary classification, accuracy can be expressed as (TP + TN) / (TP + TN + FP + FN), where TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives. In multi-class classification, accuracy is the sum of all correctly predicted instances divided by the total number of instances. Despite its simplicity, accuracy is a crucial metric for evaluating model performance, though it can be misleading in imbalanced datasets where the model might predict the majority class most of the time and still achieve high accuracy.
